Using deep learning to improve how we assemble genetic information from DNA sequences
Official title: A deep reinforcement learning framework for haplotype assembly
Funded at Broad Institute, INC. · NIH-10871190 · Data updated 2026-11-12
This study is working on a new way to piece together genetic information from DNA to help doctors better understand and treat genetic disorders, using advanced computer techniques that don't require a lot of extra data.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Broad Institute, INC. NIH-funded |
| Lab location | 1 site (Cambridge, United States) |
| Project start | July 15, 2024 |
| Project end | June 30, 2026 |
| Funding year | 2024 |
| Award (this year) | $457,920 |
| NIH project number | 1R21HG013567-01 |
| Project ID | NIH-10871190 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals with genetic disorders or those undergoing genetic testing, particularly related to conditions like autism and autoimmune diseases.
✕Less likely to help
Patients without genetic disorders or those not undergoing genetic testing may not receive direct benefits from this research.
What this research studies
This research focuses on developing a new method to accurately reconstruct genetic information from DNA sequences using deep reinforcement learning. By analyzing data from whole-genome sequencing, the project aims to improve the assembly of haplotypes, which are combinations of alleles inherited from parents. The approach leverages existing large-scale genetic datasets without needing labeled training data, making it innovative and efficient. Patients may benefit from enhanced genetic understanding that could lead to better diagnosis and treatment of genetic disorders.
Why it matters
Potential benefit: If successful, this research could lead to more accurate genetic analyses, improving diagnosis and treatment options for various genetic conditions.
How similar studies have gone: Other research has shown promise in using machine learning techniques for genetic analysis, indicating that this approach could be a significant advancement in the field.
Where this research is happening
-
Broad Institute, INC.
Cambridge, United States Active
Researchers
- Principal investigator: Popic, Victoria — Broad Institute, INC.
- Study coordinator: Popic, Victoria
Related research
Other NIH-funded projects on the same topics.
-
Broad-Color: A Center to Accelerate All of Us Genomics
A national effort to speed up personalized medicine by expanding genomic data and clinical insight for All of Us participants.
-
Common Fund data knowledge center to make big data insights easy to use
A project to make NIH Common Fund datasets easy to find, understand, and use for researchers by turning them into trusted, clearly explained knowledge that supports big data discoveries.
-
Massive-scale genomic risk assessment to inform precision medicine for diabetes
A large study aims to map how genetic differences influence different forms of diabetes to guide personalized care.
-
Massive-scale genetic risk profiling to guide personalized diabetes care
A large NIH-funded project will study how many diabetes-related genetic variants influence risk across diverse populations to help tailor prevention and treatment.
-
Asian Bipolar Genetics Network study
This project studies the genetics of bipolar disorder in East and South Asian people to better understand risk factors and how they may affect diagnosis and care.
-
Broad-Color: A Genomic Center to Accelerate All of Us for Better Health
A national genomic resource project will expand and speed up how researchers use genetic data from All of Us participants to improve understanding, diagnosis, and treatment of human diseases.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.